{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib widget\n",
    "\n",
    "from cion import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#设置使用模式\n",
    "Global_Setting.set_debug_mode(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入用户定义文件\n",
    "execfile('ZTESTER_Load_ion.py')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Ion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/********************\n",
      "Caution! You are using debug mode. This enables you to test some functions locally. But you cannot access the actual hardware in this mode.\n",
      "********************/\n",
      "test_datafile_dir\\2022\\202209\\20220914\\\n"
     ]
    }
   ],
   "source": [
    "ion_number = 1\n",
    "exp = Experiment(ion_number=ion_number, chapter_dict=Doppler_chapter_dict, port=\"COM5\")\n",
    "seq = exp.last_sequence\n",
    "print(exp.data.path_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Doppler': '00000000 00000000 00001111,[10000000 00000000 00000000, 1],[10000000 00000000 00010000,1]',\n",
       " 'Zero': '11000000 10000000 00000000',\n",
       " 'Strong': '11111111 11111111 11111111,[10000000 00000000 00000000, 1],[10000000 00000000 00010000,1]'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Doppler_chapter_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Doppler': '00000000 00000000 00000000',\n",
       " 'Doppler_Only': '00000000 00000000 00001111,[10000000 00000000 00000000, 1],[10000000 00000000 00010000,1]',\n",
       " 'Pumping': '01100000 11000000 00000000',\n",
       " 'Microwave': '11000000 10100000 00000000',\n",
       " 'Detection': '01010000 10000000 00001111,[11000000 00000000 00000000, 1],[11000000 00000000 00010000,1]',\n",
       " 'Zero': '11000000 10000000 00000000',\n",
       " 'Strong': '11111111 11111111 11111111'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exp_chapter_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.update_chapter_dict(Exp_chapter_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----sequence info----\n",
      "total pulses: 2\n",
      "sequence time: 1100 microseconds\n",
      "---------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "doppler_cooling = exp.new_sequence()\n",
    "#doppler_cooling.print_info()\n",
    "#print(doppler_cooling.ion_number)\n",
    "\n",
    "doppler_cooling.set_sequence(\n",
    "    Doppler(1000, label='Doppler').on(all),\n",
    "    Zero(100).on(all))\n",
    "\n",
    "doppler_cooling.print_info()\n",
    "\n",
    "exp.print_sequence(sequence=doppler_cooling, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['00000000 00000000 00000000', 1000], ['11000000 10000000 00000000', 100]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.last_sequence.perform_sequence_serial()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimize Doppler Cooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_datafile_dir\\2022\\202209\\20220914\\FreqScan-20220914211446.hdf5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_list = np.linspace(13485,13395,100) #MHz\n",
    "exp.freq_scan(\"Doppler\", freq_list, cycle=5) #freq_scan函数运行时会在新窗口中画图，请注意底部任务栏的弹窗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_datafile_dir\\2022\\202209\\20220914\\FreqScan-20220914211446.hdf5\n",
      "501\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "print(exp.data.DataFileName)\n",
    "a,b = raw_count(exp.data.DataFileName)\n",
    "print(len(a))\n",
    "print(b[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advance Gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "execfile('sequencies.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/********************\n",
      "Caution! You are using debug mode. This enables you to test some functions locally. But you cannot access the actual hardware in this mode.\n",
      "********************/\n"
     ]
    }
   ],
   "source": [
    "exp_new = Experiment(ion_number=4, chapter_dict=virtual_chapter_dict, port=\"COM5\", ip_address='192.168.2.30')\n",
    "seq = exp_new.last_sequence\n",
    "seq.awg_sampling_rate = 625\n",
    "\n",
    "prev_sequence = [EIT(100).on(all),Doppler(100).on(all)]\n",
    "\n",
    "#这是个分段函数，时间上分成了三段，而每个时间段又有两个sin分量\n",
    "#比如第一个离子在(0, 17.33us)这个区间，其awg生成函数为f(t) = 0.45*sin((4+2.03)t + 0.48)) + 0.45*sin((4-2.03)t - 0.48))\n",
    "#但是这一功能在早期实验中可能不需要用到\n",
    "#\n",
    "dict0 = {\n",
    "    'ion_number' : 4,\n",
    "    'segment_number' : 3,\n",
    "    'time_intervals' : [(0,17.33),(17.33, 45.66),(45.66, 87.99)],\n",
    "    'data_per_ion' : {\n",
    "        #第一个离子的参数表\n",
    "        0 : {\n",
    "            'amp': [(0.45,0.45), (0.42,0.42),(0.23,0.23)], \n",
    "            'freq': [(4+2.03,4-2.03),(4+2.03,4-2.03),(4+2.03,4-2.03)],\n",
    "            'phase': [(0.48,-0.48),(0.308,-0.308),(0.086,-0.086)]\n",
    "        },\n",
    "        #第二个离子的参数表\n",
    "        1 : {\n",
    "            'amp': [(0.42,0.42),(0.23,0.23),(0.45,0.45,0.10)], \n",
    "            'freq': [(4+2.03,4-2.03),(4+2.03,4-2.03),(4+2.03,4-2.03,2.03)],\n",
    "            'phase': [(0.308,-0.308),(0.086,-0.086),(-0.086,0.086,0.086)]\n",
    "        },\n",
    "        #第三个离子的参数表\n",
    "        2 : {\n",
    "            'amp': [(0.45,0.45), (0.42,0.42),(0.23,0.23)], \n",
    "            'freq': [(4+2.03,4-2.03),(4+2.03,4-2.03),(4+2.03,4-2.03)],\n",
    "            'phase': [(0.308,-0.308),(0.086,-0.086),(-0.086,0.086)]\n",
    "        }\n",
    "    }\n",
    "    \n",
    "}\n",
    "\n",
    "exp_new.last_sequence.set_sequence( prev_sequence, \n",
    "                  awg_trigger(),\n",
    "                  Rx(20, awg_flag=True,amp = 0.2, phase = 2.4, freq = 3.6, label='rx0').on(0),\n",
    "                  Rx(5, awg_flag=True,amp = 0.0, phase = 2.4, freq = 3.6,label='rx1').on(0), \n",
    "                  Rx(20, awg_flag=True,amp = 0.2, phase = 2.4, freq = 3.6,label='rx1').on(0), \n",
    "                  Rx(5, awg_flag=True,amp = 0.2, phase = 2.4, freq = 3.6,label='rx1').on(1), \n",
    "                  Rx(50, awg_flag=True,amp = 0.2, phase = 2.4, freq = 3.6,label='rx1').on(3),\n",
    "                  sync(all),\n",
    "                  Rx(5, awg_flag=True,amp = 0.2, phase = 2.4, freq = 3.6,label='rx1').on(2), \n",
    "                  sync(all),\n",
    "                  Detection(500, label = 'detect').on(all),\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sequence(seq.gate_sequence, auto_size=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1944x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_new.send_awg_data(sequence=seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(253125,)\n",
      "(253125,)\n",
      "(253125,)\n",
      "(253125,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4, 253125)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(seq.awg_data[0].shape)\n",
    "print(seq.awg_data[1].shape)\n",
    "print(seq.awg_data[2].shape)\n",
    "print(seq.awg_data[3].shape)\n",
    "seq.awg_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
